AIC average by age group

Run regressions between model parameters and age

## 
## Call:
## lm(formula = LL ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -154.578  -58.515    9.859   54.104  200.722 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -215.354     19.598  -10.99   <2e-16 ***
## age            1.591      1.170    1.36    0.176    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 72.28 on 148 degrees of freedom
## Multiple R-squared:  0.01235,    Adjusted R-squared:  0.005673 
## F-statistic:  1.85 on 1 and 148 DF,  p-value: 0.1758
## 
## Call:
## lm(formula = alphaPosChoice ~ age, data = model_params)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.3090 -0.1999 -0.0917  0.1265  0.6863 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.320756   0.074344   4.314 2.91e-05 ***
## age         -0.001369   0.004438  -0.308    0.758    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2742 on 148 degrees of freedom
## Multiple R-squared:  0.0006424,  Adjusted R-squared:  -0.00611 
## F-statistic: 0.09514 on 1 and 148 DF,  p-value: 0.7582
## 
## Call:
## lm(formula = alphaNegChoice ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.20679 -0.12581 -0.06585  0.00345  0.81559 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.304868   0.061239   4.978 1.76e-06 ***
## age         -0.011916   0.003656  -3.260  0.00138 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2259 on 148 degrees of freedom
## Multiple R-squared:  0.06698,    Adjusted R-squared:  0.06068 
## F-statistic: 10.62 on 1 and 148 DF,  p-value: 0.001385
## 
## Call:
## lm(formula = alphaPosComp ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.23170 -0.17898 -0.12042  0.05715  0.87437 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.300954   0.073408   4.100  6.8e-05 ***
## age         -0.007638   0.004382  -1.743   0.0834 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2707 on 148 degrees of freedom
## Multiple R-squared:  0.02012,    Adjusted R-squared:  0.01349 
## F-statistic: 3.038 on 1 and 148 DF,  p-value: 0.08341
## 
## Call:
## lm(formula = alphaNegComp ~ age, data = model_params)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.2432 -0.2170 -0.1918  0.2049  0.7903 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.270545   0.088504   3.057  0.00265 **
## age         -0.003096   0.005284  -0.586  0.55877   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3264 on 148 degrees of freedom
## Multiple R-squared:  0.002315,   Adjusted R-squared:  -0.004426 
## F-statistic: 0.3434 on 1 and 148 DF,  p-value: 0.5588
## 
## Call:
## lm(formula = betaAgency ~ age, data = model_params)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.158 -4.012 -1.747  2.933 20.338 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  3.53843    1.51875   2.330  0.02117 * 
## age          0.24547    0.09067   2.707  0.00758 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.601 on 148 degrees of freedom
## Multiple R-squared:  0.04719,    Adjusted R-squared:  0.04075 
## F-statistic:  7.33 on 1 and 148 DF,  p-value: 0.007579
## 
## Call:
## lm(formula = betaMachine ~ age, data = model_params)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.833 -3.151 -1.048  1.911 22.856 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   4.9326     1.3383   3.686 0.000319 ***
## age           0.1402     0.0799   1.755 0.081273 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.936 on 148 degrees of freedom
## Multiple R-squared:  0.02039,    Adjusted R-squared:  0.01378 
## F-statistic: 3.081 on 1 and 148 DF,  p-value: 0.08127
## 
## Call:
## lm(formula = agencyBonus ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.72271 -0.30021 -0.19730  0.08013  2.37437 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.72607    0.18718   3.879 0.000157 ***
## age         -0.01312    0.01117  -1.174 0.242346    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6904 on 148 degrees of freedom
## Multiple R-squared:  0.009224,   Adjusted R-squared:  0.00253 
## F-statistic: 1.378 on 1 and 148 DF,  p-value: 0.2423

Plot relations between model parameters and age

Parameter summary statistics

Beta model

## Mixed Model Anova Table (Type 3 tests, S-method)
## 
## Model: estimate ~ ageZ * betaType + (1 | subject_id)
## Data: betas
##          Effect        df      F p.value
## 1          ageZ 1, 148.00 6.75 *    .010
## 2      betaType 1, 148.00   0.45    .503
## 3 ageZ:betaType 1, 148.00   1.55    .216
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: estimate ~ ageZ * betaType + (1 | subject_id)
##    Data: data
## 
## REML criterion at convergence: 1803.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.9989 -0.5011 -0.1486  0.4083  3.2037 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  subject_id (Intercept) 14.21    3.770   
##  Residual               13.66    3.696   
## Number of obs: 300, groups:  subject_id, 150
## 
## Fixed effects:
##                Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)      7.3161     0.3745 148.0000  19.534   <2e-16 ***
## ageZ             0.9745     0.3752 148.0000   2.597   0.0103 *  
## betaType1        0.1433     0.2134 148.0000   0.672   0.5028    
## ageZ:betaType1   0.2658     0.2137 148.0000   1.244   0.2155    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) ageZ  btTyp1
## ageZ        0.000              
## betaType1   0.000  0.000       
## ageZ:btTyp1 0.000  0.000 0.000
Predictor Estimates SE Statistic p
intercept 7.32 0.37 19.53 <0.001
age 0.97 0.38 2.60 0.010
decision stage 0.14 0.21 0.67 0.502
age x decision stage 0.27 0.21 1.24 0.215

Learning rate model

## Mixed Model Anova Table (Type 3 tests, S-method)
## 
## Model: estimate ~ age_z * valence * agency + (1 | subject_id)
## Data: learning_rates
##                 Effect        df         F p.value
## 1                age_z 1, 148.00    5.38 *    .022
## 2              valence 1, 444.00 11.17 ***   <.001
## 3               agency 1, 444.00      0.10    .753
## 4        age_z:valence 1, 444.00      0.51    .477
## 5         age_z:agency 1, 444.00      0.09    .762
## 6       valence:agency 1, 444.00 28.34 ***   <.001
## 7 age_z:valence:agency 1, 444.00    3.20 +    .074
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: estimate ~ age_z * valence * agency + (1 | subject_id)
##    Data: data
## 
## REML criterion at convergence: 199.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.5327 -0.6127 -0.3271  0.2431  3.2261 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  subject_id (Intercept) 0.008624 0.09287 
##  Residual               0.067885 0.26055 
## Number of obs: 600, groups:  subject_id, 150
## 
## Fixed effects:
##                          Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)              0.203361   0.013063 148.000000  15.568  < 2e-16 ***
## age_z                   -0.030316   0.013074 148.000000  -2.319   0.0218 *  
## valence1                -0.035555   0.010637 444.000000  -3.343   0.0009 ***
## agency1                  0.003345   0.010637 444.000000   0.314   0.7533    
## age_z:valence1          -0.007579   0.010646 444.000000  -0.712   0.4769    
## age_z:agency1           -0.003219   0.010646 444.000000  -0.302   0.7625    
## valence1:agency1        -0.056629   0.010637 444.000000  -5.324 1.62e-07 ***
## age_z:valence1:agency1  -0.019045   0.010646 444.000000  -1.789   0.0743 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) age_z valnc1 agncy1 ag_z:v1 ag_z:g1 vln1:1
## age_z       0.000                                            
## valence1    0.000  0.000                                     
## agency1     0.000  0.000 0.000                               
## age_z:vlnc1 0.000  0.000 0.000  0.000                        
## age_z:gncy1 0.000  0.000 0.000  0.000  0.000                 
## vlnc1:gncy1 0.000  0.000 0.000  0.000  0.000   0.000         
## ag_z:vln1:1 0.000  0.000 0.000  0.000  0.000   0.000   0.000
## 
##  Paired t-test
## 
## data:  model_params$alphaPosChoice and model_params$alphaNegChoice
## t = 6.9666, df = 149, p-value = 9.73e-11
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  0.1320728 0.2366607
## sample estimates:
## mean difference 
##       0.1843667
## 
##  Paired t-test
## 
## data:  model_params$alphaPosComp and model_params$alphaNegComp
## t = -1.1108, df = 149, p-value = 0.2685
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  -0.11712714  0.03283236
## sample estimates:
## mean difference 
##     -0.04214739
Predictor Estimates SE Statistic p
intercept 0.20 0.01 15.57 <0.001
age -0.03 0.01 -2.32 0.021
valence -0.04 0.01 -3.34 0.001
agency 0.00 0.01 0.31 0.753
age x valence -0.01 0.01 -0.71 0.477
age x agency -0.00 0.01 -0.30 0.762
valence x agency -0.06 0.01 -5.32 <0.001
age x valence x agency -0.02 0.01 -1.79 0.074

Learning rate plot

Relation between parameter estimates and ‘model-free’ regressions

## 
## Call:
## lm(formula = `(Intercept)` ~ agencyBonus, data = voc_REs_RL)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9134 -0.6544  0.0318  0.7376  5.8069 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -1.2960     0.1436  -9.025 8.77e-16 ***
## agencyBonus   2.4096     0.1668  14.449  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.407 on 148 degrees of freedom
## Multiple R-squared:  0.5852, Adjusted R-squared:  0.5824 
## F-statistic: 208.8 on 1 and 148 DF,  p-value: < 2.2e-16
## 
## Call:
## lm(formula = voc_z ~ betaAgency, data = voc_REs_RL)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.13455 -0.22221 -0.03153  0.14601  1.68526 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.779560   0.049299  -15.81   <2e-16 ***
## betaAgency   0.102000   0.005251   19.42   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3666 on 148 degrees of freedom
## Multiple R-squared:  0.7182, Adjusted R-squared:  0.7163 
## F-statistic: 377.3 on 1 and 148 DF,  p-value: < 2.2e-16
## 
## Call:
## lm(formula = voc_z ~ betaAgency + age, data = voc_REs_RL)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.16014 -0.23331 -0.02059  0.18482  1.67324 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.967014   0.099995  -9.671   <2e-16 ***
## betaAgency   0.099521   0.005315  18.723   <2e-16 ***
## age          0.012893   0.006006   2.147   0.0335 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3622 on 147 degrees of freedom
## Multiple R-squared:  0.7268, Adjusted R-squared:  0.7231 
## F-statistic: 195.5 on 2 and 147 DF,  p-value: < 2.2e-16
## 
## Call:
## lm(formula = voc_z ~ betaAgency + betaMachine, data = voc_REs_RL)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.13574 -0.23276 -0.02974  0.13115  1.68646 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.786072   0.056240 -13.977   <2e-16 ***
## betaAgency   0.101204   0.006202  16.319   <2e-16 ***
## betaMachine  0.001736   0.007136   0.243    0.808    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3678 on 147 degrees of freedom
## Multiple R-squared:  0.7184, Adjusted R-squared:  0.7145 
## F-statistic: 187.5 on 2 and 147 DF,  p-value: < 2.2e-16
---
title: "E2 VoC Analyses Part 3: RL Analyses"
date: 3/27/24
output:
    html_document:
        df_print: 'paged'
        toc: true
        toc_float:
            collapsed: false
            smooth_scroll: true
        number_sections: false
        code_download: true
        self_contained: true
---

```{r chunk settings, include = FALSE}
# set chunk settings
knitr::opts_chunk$set(echo = FALSE, 
                      cache = TRUE,
                      message = FALSE,
                      warning = FALSE)
knitr::opts_chunk$set(dpi=600)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
```

```{r load libraries, include = F}

#load libraries
library(tidyverse)
library(glue)
library(afex)
library(latex2exp)
library(sjPlot)

#load scripts
source('analysis_scripts/voc_functions.R')
```

```{r import data}
# read in participant ages
participant_ages <- read_csv('data/voc_sub_info.csv') 

## read in aics
aics = read_csv("RL_modeling/output/aics_all_16_models_100iter.csv") %>%
  rename(subject_id = subID)

# combine with ages
aics <- inner_join(aics, participant_ages, by = 'subject_id') %>%
  mutate(age_group = case_when(age < 13 ~ "Children",
                               age > 12.99 & age < 18 ~ "Adolescents",
                               age > 17.99 ~ "Adults"))

aics$age_group <- factor(aics$age_group, levels = c("Children", "Adolescents", "Adults"))
         

#pivot longer
model_results <- pivot_longer(aics, 
                      cols = oneAlpha_oneBeta:fourAlpha_twoBeta_agencyBonus,
                      names_to = "model",
                      values_to = "AIC")


model_results$model <- factor(model_results$model, 
                              levels = c("oneAlpha_oneBeta",
                                         "oneAlpha_twoBeta",
                                         "twoAlpha_oneBeta",
                                         "twoAlpha_twoBeta",
                                         "twoAlphaValenced_oneBeta",
                                         "twoAlphaValenced_twoBeta",
                                         "fourAlpha_oneBeta",
                                         "fourAlpha_twoBeta",
                                         "oneAlpha_oneBeta_agencyBonus",
                                         "oneAlpha_twoBeta_agencyBonus",
                                         "twoAlpha_oneBeta_agencyBonus",
                                         "twoAlpha_twoBeta_agencyBonus",
                                         "twoAlphaValenced_oneBeta_agencyBonus",
                                         "twoAlphaValenced_twoBeta_agencyBonus",
                                         "fourAlpha_oneBeta_agencyBonus",
                                         "fourAlpha_twoBeta_agencyBonus"))
model_results <- model_results %>%
  mutate(agencyBonus = case_when(str_detect(model, "agency") ~ "With Agency Bonus",
                                 !str_detect(model, "agency") ~ "No Agency Bonus"),
         shortName = str_remove(model, '_agencyBonus'))

model_results$shortName <- factor(model_results$shortName,
                                  levels = c("oneAlpha_oneBeta",
                                             "oneAlpha_twoBeta",
                                             "twoAlpha_oneBeta",
                                             "twoAlpha_twoBeta",
                                             "twoAlphaValenced_oneBeta",
                                             "twoAlphaValenced_twoBeta",
                                             "fourAlpha_oneBeta",
                                             "fourAlpha_twoBeta"))
```

#  AIC average by age group 
```{r plot AIC by age group, fig.width = 8, fig.height = 5, units = "in"}
#summarize
model_summary <- model_results %>%
  group_by(age_group, shortName, agencyBonus) %>%
  summarize(mean_aic = mean(AIC))

## Plot the results by age group 
AIC_age_plot <- ggplot(model_summary, aes(x = age_group, y = mean_aic, fill = shortName))+
  facet_wrap(~agencyBonus) +
  geom_bar(stat = "identity", position = "dodge", color = "black") +
  scale_fill_manual(name = "Model",
                    values = c(color8, color1, color2, color3, color4, color5, color6, color7, color1),
                    labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
  coord_cartesian(ylim = c(350, 650)) +
  ylab("Mean AIC") +
  xlab("") +
  voc_theme() +
  theme(axis.text.x = element_text(angle = 60, hjust = 1))
AIC_age_plot
```


#  Examine age-related change in parameter estimates from models
```{r load parameters from winning model}
model_params <- read_csv("RL_modeling/output/model_fits_real_data/fourAlpha_twoBeta_agencyBonus.csv",
                         col_names = c("negLL",
                                       "logPost",
                                       "AIC",
                                       "BIC",
                                       "alphaPosChoice",
                                       "alphaNegChoice",
                                       "alphaPosComp",
                                       "alphaNegComp",
                                       "betaAgency",
                                       "betaMachine",
                                       "agencyBonus"))

#add sub ID and information
subject_id <- model_results %>% select(subject_id) %>% unique()
model_params <- bind_cols(subject_id, model_params)
model_params <- inner_join(participant_ages, model_params, by = c("subject_id"))

```


# Run regressions between model parameters and age
```{r parameter regressions}
model_params$LL <- model_params$negLL * -1

# Log likelihood
summary(lm(LL ~ age, data = model_params))
# not significant

# Alpha Pos Choice
summary(lm(alphaPosChoice ~ age, data = model_params))
#not significant

# Alpha Neg Choice
summary(lm(alphaNegChoice ~ age, data = model_params))
# significant

# Alpha Pos Comp
summary(lm(alphaPosComp ~ age, data = model_params))
#not significant

# Alpha Neg Comp
summary(lm(alphaNegComp ~ age, data = model_params))
#not significant

# Beta Agency
summary(lm(betaAgency ~ age, data = model_params))
# significant

# Beta Bandit
summary(lm(betaMachine ~ age, data = model_params))
# not significant

# agency bonus
summary(lm(agencyBonus ~ age, data = model_params))
# not significant

```



# Plot relations between model parameters and age
```{r age parameter plot, fig.width = 7, fig.height = 4, units = "in"}

params_long <- model_params %>%
  pivot_longer(names_to = "param",
               values_to = "estimate",
               cols = c(alphaPosChoice:agencyBonus)) 

params_long$param <- factor(params_long$param, 
                            levels = c("alphaPosChoice",
                                       "alphaNegChoice",
                                       "alphaPosComp",
                                       "alphaNegComp",
                                       "betaAgency",
                                       "betaMachine",
                                       "agencyBonus"),
                            labels = c(TeX("$\\alpha_{choice_+}$"), 
                                       TeX("$\\alpha_{choice_-}$"), 
                                       TeX("$\\alpha_{comp_+}$"), 
                                       TeX("$\\alpha_{comp_-}$"), 
                                       TeX("$\\beta_{agency}$"), 
                                       TeX("$\\beta_{machine}$"),
                                       "Agency~Bonus"
                            ))

params_plot <- ggplot(params_long, aes(x = age, y = estimate, color = param)) +
  facet_wrap(~param, scale = "free", labeller = label_parsed, nrow = 2) +
  geom_point() +
  geom_smooth(method = "lm", aes(fill = param)) +
  ylab("Parameter Estimate") +
  xlab("Age") +
  voc_theme() +
  theme(legend.position = "none")
params_plot
```


# Parameter summary statistics
```{r parameter summary stats}

param_summary <- params_long %>%
    group_by(param) %>%
    summarize(meanEstimate = mean(estimate),
            seEstimate = sd(estimate)/sqrt(n()))
param_summary

```

# Beta model
```{r beta regression}
betas <- model_params %>%
    pivot_longer(cols = c(betaAgency, betaMachine),
                 names_to = "betaType",
                 values_to = "estimate") %>%
    select(subject_id, age, betaType, estimate) %>%
    unique() 
                               
betas$ageZ <- scale_this(betas$age)

beta_model <- mixed(estimate ~ ageZ * betaType + (1|subject_id),
                             data = betas,
                             method = "S")
beta_model
summary(beta_model)

```

```{r  beta print model stats}

beta_lmer <- mixed(estimate ~ ageZ * betaType + (1|subject_id),
                   data = betas,
                   method = "S",
                   return = "merMod")

tab_model(beta_lmer, 
          pred.labels = c("intercept", "age", "decision stage", "age x decision stage"),
          transform = NULL,
          show.est = T, 
          show.se = T, 
          show.stat = T,
          show.ci = F,
          show.re.var = F,
          show.icc = F,
          show.ngroups = F,
          show.obs = F,
          show.r2 = F,
          string.se = "SE",
          emph.p = F,
          string.pred = "Predictor",
          title = "",
          dv.labels = "")
```

# Learning rate model
```{r learning rate regression}
## Learning rate model
learning_rates <- model_params %>%
  pivot_longer(cols = c(alphaPosChoice:alphaNegComp),
               names_to = "learningRate",
               values_to = "estimate") %>%
  select(subject_id, age, learningRate, estimate) %>%
  unique() %>%
  mutate(valence = case_when(str_detect(learningRate, "Pos") ~ "Positive",
                             str_detect(learningRate, "Neg") ~ "Negative"),
         agency = case_when(str_detect(learningRate, "Choice") ~ "Choice",
                            str_detect(learningRate, "Comp") ~ "Comp"))

learning_rates$age_z <- scale_this(learning_rates$age)

learning_rate_model <- mixed(estimate ~ age_z * valence * agency + (1|subject_id),
                             data = learning_rates,
                             method = "S")
learning_rate_model
summary(learning_rate_model)
# main effect of age
# main effect of valence
# valence x agency interaction


#t test between alpha pos choice and alpha neg choice
t.test(model_params$alphaPosChoice, model_params$alphaNegChoice, paired = T)
#significant

#t test between alpha pos comp and alpha neg comp
t.test(model_params$alphaPosComp, model_params$alphaNegComp, paired = T)
#not significant

```


```{r learning rate print model stats}

learning_rate_lmer <- mixed(estimate ~ age_z * valence * agency + (1|subject_id),
                             data = learning_rates,
                             method = "S",
                            return = "merMod")

tab_model(learning_rate_lmer, 
          pred.labels = c("intercept", "age", "valence", "agency", "age x valence", "age x agency", "valence x agency", "age x valence x agency"),
          transform = NULL,
          show.est = T, 
          show.se = T, 
          show.stat = T,
          show.ci = F,
          show.re.var = F,
          show.icc = F,
          show.ngroups = F,
          show.obs = F,
          show.r2 = F,
          string.se = "SE",
          emph.p = F,
          string.pred = "Predictor",
          title = "",
          dv.labels = "")
```

## Learning rate plot
```{r learning rate plot}

learning_rate_means <- learning_rates %>%
    group_by(agency, valence) %>%
    summarize(meanLR = mean(estimate),
              seLR = sd(estimate) / sqrt(n()))

learning_rate_plot <- ggplot(learning_rate_means, aes(x = agency, y = meanLR, fill = valence)) +
    geom_bar(color = 'black', stat = "identity", position = "dodge") + 
    geom_errorbar(color = "black", aes(ymin = meanLR - seLR, ymax = meanLR + seLR), width = .1,
                  position = position_dodge(width = .9)) +
    scale_fill_manual(values = c(color1, color2), name = "Valence") +
    ylab("Mean Learning Rate") +
    xlab("Agency") +
    scale_x_discrete(labels = c("Participant Choice", "Computer Choice")) +
    voc_theme()
learning_rate_plot 
```



# Relation between parameter estimates and 'model-free' regressions
```{r does the voc by age interaction effect relate to betaAgency}

# Read in data
learning_data <- read_csv('data/processed/learning_data.csv') 

#combine with participant age
learning_data <- full_join(learning_data, participant_ages, by = c("subject_id"))

#process 
learning_data <- learning_data %>%
  mutate(ev_choice = case_when(context == 0 ~ 9,
                               context == 1 ~ 7,
                               context == 2 ~ 5),
         ev_comp = 5 + offer,
         voc = ev_choice - ev_comp,
         better_machine = case_when(reward_prob_L > reward_prob_R ~ 1,
                                    reward_prob_L < reward_prob_R ~ 0,
         ),
         stage_2_acc = case_when(stage_2_choice == better_machine ~ 1,
                                 stage_2_choice != better_machine ~ 0)) %>%
  group_by(subject_id, context) %>%
  mutate(condition_trial = rank(trial),
         block = floor((trial-1)/21 + 1))

# exclude first-stage misses and first-stage RT < 150 ms
learning_data_filtered <- learning_data %>%
  filter(stage_1_rt > 150)

#get agency model data
agency_model_data <- learning_data_filtered %>%
  select(subject_id, stage_1_choice, voc, condition_trial, block, trial, age)

## REGRESSION MODEL ##
#z score continuous variables
agency_model_data$subject_id <- factor(agency_model_data$subject_id)
agency_model_data$voc_z <- scale_this(agency_model_data$voc)
agency_model_data$condition_trial <- scale_this(agency_model_data$condition_trial)
agency_model_data$age_z <- scale_this(agency_model_data$age)

# predict agency choice from utility of control, trial, linear age
agency_byVOCTrialAge.glmer = mixed(stage_1_choice ~ voc_z * condition_trial + (voc_z * condition_trial | subject_id), 
                        data = agency_model_data, 
                        family = binomial, 
                        method = "LRT", control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=1e6)),
                        return = "merMod") 

#get random effects
voc_REs <- ranef(agency_byVOCTrialAge.glmer)$subject_id %>%
    rownames_to_column(var = "subject_id")

voc_REs$subject_id <- as.numeric(voc_REs$subject_id)

#combine with RL estimates
voc_REs_RL <- full_join(voc_REs, model_params, by = 'subject_id')

```


```{r run RE and parameter regressions}

#run regressions

#agency bonus
voc_RE_agencyBonus.lm <- lm(`(Intercept)` ~ agencyBonus, data = voc_REs_RL)
summary(voc_RE_agencyBonus.lm)

#beta agency
voc_RE_betaAgency.lm <- lm(voc_z ~ betaAgency, data = voc_REs_RL)
summary(voc_RE_betaAgency.lm)

#control for age
voc_RE_betaAgencyAge.lm <- lm(voc_z ~ betaAgency + age, data = voc_REs_RL)
summary(voc_RE_betaAgencyAge.lm)

#control for beta machine
voc_RE_betaMachine.lm <- lm(voc_z ~ betaAgency + betaMachine, data = voc_REs_RL)
summary(voc_RE_betaMachine.lm)


```
